Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations141250
Missing cells893520
Missing cells (%)27.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.1 MiB
Average record size in memory965.6 B

Variable types

Text6
Categorical10
Unsupported1
Numeric4
Boolean1
DateTime1

Alerts

Card Type has constant value "Credit" Constant
Bank Account Type has constant value "Checking" Constant
Bank Name has constant value "Bank of Example" Constant
AVS Result Code has constant value "True" Constant
CVV Result Code has constant value "M" Constant
Card Brand is highly overall correlated with Payment Gateway NameHigh correlation
Card Last 4 Digits is highly overall correlated with Payment Gateway NameHigh correlation
Gateway Fees is highly overall correlated with Net Settled Amount and 2 other fieldsHigh correlation
Gateway Response Message is highly overall correlated with Net Settled Amount and 3 other fieldsHigh correlation
Net Settled Amount is highly overall correlated with Gateway Fees and 3 other fieldsHigh correlation
Payment Gateway Name is highly overall correlated with Card Brand and 2 other fieldsHigh correlation
Transaction Amount is highly overall correlated with Gateway Fees and 3 other fieldsHigh correlation
Transaction Status is highly overall correlated with Gateway Response Message and 1 other fieldsHigh correlation
Transaction Type is highly overall correlated with Gateway Response Message and 3 other fieldsHigh correlation
Transaction Status is highly imbalanced (82.3%) Imbalance
Gateway Response Message is highly imbalanced (68.6%) Imbalance
Transaction Type is highly imbalanced (68.6%) Imbalance
Card Brand has 93954 (66.5%) missing values Missing
Card Last 4 Digits has 93954 (66.5%) missing values Missing
Card Type has 93954 (66.5%) missing values Missing
Token ID has 46844 (33.2%) missing values Missing
Bank Account Type has 94406 (66.8%) missing values Missing
Bank Name has 94406 (66.8%) missing values Missing
PayPal Payer ID has 94140 (66.6%) missing values Missing
AVS Result Code has 93954 (66.5%) missing values Missing
CVV Result Code has 93954 (66.5%) missing values Missing
Acquirer Reference Number (ARN) has 93954 (66.5%) missing values Missing
reference_number has unique values Unique
Gateway Response Code is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-06 23:53:25.569030
Analysis finished2025-07-06 23:53:31.421588
Duration5.85 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct140145
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2025-07-06T16:53:31.647759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1977500
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139043 ?
Unique (%)98.4%

Sample

1st rowGW-TRX-9972261
2nd rowGW-TRX-8455695
3rd rowGW-TRX-5184318
4th rowGW-TRX-8104962
5th rowGW-TRX-2770667
ValueCountFrequency (%)
gw-trx-7907860 3
 
< 0.1%
gw-trx-7322359 3
 
< 0.1%
gw-trx-6671195 3
 
< 0.1%
gw-trx-4056314 2
 
< 0.1%
gw-trx-2412164 2
 
< 0.1%
gw-trx-6392152 2
 
< 0.1%
gw-trx-2151894 2
 
< 0.1%
gw-trx-9560362 2
 
< 0.1%
gw-trx-8952714 2
 
< 0.1%
gw-trx-1465885 2
 
< 0.1%
Other values (140135) 141227
> 99.9%
2025-07-06T16:53:31.847062image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 282500
14.3%
G 141250
 
7.1%
W 141250
 
7.1%
T 141250
 
7.1%
R 141250
 
7.1%
X 141250
 
7.1%
2 101031
 
5.1%
3 100708
 
5.1%
4 100656
 
5.1%
6 100336
 
5.1%
Other values (6) 586019
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 282500
14.3%
G 141250
 
7.1%
W 141250
 
7.1%
T 141250
 
7.1%
R 141250
 
7.1%
X 141250
 
7.1%
2 101031
 
5.1%
3 100708
 
5.1%
4 100656
 
5.1%
6 100336
 
5.1%
Other values (6) 586019
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 282500
14.3%
G 141250
 
7.1%
W 141250
 
7.1%
T 141250
 
7.1%
R 141250
 
7.1%
X 141250
 
7.1%
2 101031
 
5.1%
3 100708
 
5.1%
4 100656
 
5.1%
6 100336
 
5.1%
Other values (6) 586019
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 282500
14.3%
G 141250
 
7.1%
W 141250
 
7.1%
T 141250
 
7.1%
R 141250
 
7.1%
X 141250
 
7.1%
2 101031
 
5.1%
3 100708
 
5.1%
4 100656
 
5.1%
6 100336
 
5.1%
Other values (6) 586019
29.6%

Payment Gateway Name
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Cybersource
47296 
PayPal
47110 
GoCardless
46844 

Length

Max length11
Median length10
Mean length9.0007504
Min length6

Characters and Unicode

Total characters1271356
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowCybersource
5th rowPayPal

Common Values

ValueCountFrequency (%)
Cybersource 47296
33.5%
PayPal 47110
33.4%
GoCardless 46844
33.2%

Length

2025-07-06T16:53:31.881406image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:31.901656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
cybersource 47296
33.5%
paypal 47110
33.4%
gocardless 46844
33.2%

Most occurring characters

ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Transaction Status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
settled
137500 
disputed
 
3750

Length

Max length8
Median length7
Mean length7.0265487
Min length7

Characters and Unicode

Total characters992500
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsettled
2nd rowsettled
3rd rowsettled
4th rowsettled
5th rowsettled

Common Values

ValueCountFrequency (%)
settled 137500
97.3%
disputed 3750
 
2.7%

Length

2025-07-06T16:53:31.934428image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:31.954715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
settled 137500
97.3%
disputed 3750
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Gateway Response Code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.5 MiB

Gateway Response Message
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Approved
125000 
Refund Processed
 
12500
Chargeback Initiated
 
2500
Chargeback Reversed
 
1250

Length

Max length20
Median length8
Mean length9.0176991
Min length8

Characters and Unicode

Total characters1273750
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Approved 125000
88.5%
Refund Processed 12500
 
8.8%
Chargeback Initiated 2500
 
1.8%
Chargeback Reversed 1250
 
0.9%

Length

2025-07-06T16:53:31.983597image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.006566image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
approved 125000
79.4%
refund 12500
 
7.9%
processed 12500
 
7.9%
chargeback 3750
 
2.4%
initiated 2500
 
1.6%
reversed 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Transaction Amount
Real number (ℝ)

High correlation 

Distinct122801
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.2208
Minimum-4999.85
Maximum4999.87
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:53:32.043186image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4999.85
5-th percentile-2655.288
Q1828.465
median2221.17
Q33607.4425
95-th percentile4724.491
Maximum4999.87
Range9999.72
Interquartile range (IQR)2778.9775

Descriptive statistics

Standard deviation2112.685
Coefficient of variation (CV)1.0674327
Kurtosis1.0466637
Mean1979.2208
Median Absolute Deviation (MAD)1389.74
Skewness-1.0062879
Sum2.7956493 × 108
Variance4463437.9
MonotonicityNot monotonic
2025-07-06T16:53:32.084191image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4756.12 5
 
< 0.1%
953.97 5
 
< 0.1%
931.3 5
 
< 0.1%
3543.24 5
 
< 0.1%
4146.9 5
 
< 0.1%
1020.37 5
 
< 0.1%
1902 5
 
< 0.1%
2595.9 5
 
< 0.1%
1121 4
 
< 0.1%
4540.24 4
 
< 0.1%
Other values (122791) 141202
> 99.9%
ValueCountFrequency (%)
-4999.85 1
< 0.1%
-4999.06 1
< 0.1%
-4998.84 2
< 0.1%
-4998.76 1
< 0.1%
-4998.23 1
< 0.1%
-4996.55 2
< 0.1%
-4996.24 1
< 0.1%
-4996.23 2
< 0.1%
-4996.21 2
< 0.1%
-4995.93 1
< 0.1%
ValueCountFrequency (%)
4999.87 1
< 0.1%
4999.85 2
< 0.1%
4999.66 1
< 0.1%
4999.61 1
< 0.1%
4999.54 1
< 0.1%
4999.44 1
< 0.1%
4999.28 2
< 0.1%
4999.27 1
< 0.1%
4999.26 1
< 0.1%
4999.24 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
USD
84784 
EUR
21002 
GBP
14100 
AUD
11416 
CAD
9948 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters423750
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 84784
60.0%
EUR 21002
 
14.9%
GBP 14100
 
10.0%
AUD 11416
 
8.1%
CAD 9948
 
7.0%

Length

2025-07-06T16:53:32.120913image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.147336image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 84784
60.0%
eur 21002
 
14.9%
gbp 14100
 
10.0%
aud 11416
 
8.1%
cad 9948
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Transaction Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
payment
125000 
refund
 
12500
chargeback
 
2500
chargeback_reversal
 
1250

Length

Max length19
Median length7
Mean length7.0707965
Min length6

Characters and Unicode

Total characters998750
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpayment
2nd rowpayment
3rd rowpayment
4th rowpayment
5th rowpayment

Common Values

ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Length

2025-07-06T16:53:32.181736image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.206082image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Card Brand
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size7.5 MiB
Mastercard
15960 
Visa
15697 
Amex
15639 

Length

Max length10
Median length4
Mean length6.0246955
Min length4

Characters and Unicode

Total characters284944
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVisa
2nd rowMastercard
3rd rowAmex
4th rowMastercard
5th rowVisa

Common Values

ValueCountFrequency (%)
Mastercard 15960
 
11.3%
Visa 15697
 
11.1%
Amex 15639
 
11.1%
(Missing) 93954
66.5%

Length

2025-07-06T16:53:32.235137image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.257995image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
mastercard 15960
33.7%
visa 15697
33.2%
amex 15639
33.1%

Most occurring characters

ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Card Last 4 Digits
Real number (ℝ)

High correlation  Missing 

Distinct8963
Distinct (%)19.0%
Missing93954
Missing (%)66.5%
Infinite0
Infinite (%)0.0%
Mean5511.9391
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:53:32.289092image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1459.75
Q13267
median5540.5
Q37743.25
95-th percentile9541
Maximum9999
Range8999
Interquartile range (IQR)4476.25

Descriptive statistics

Standard deviation2588.5975
Coefficient of variation (CV)0.46963463
Kurtosis-1.1929835
Mean5511.9391
Median Absolute Deviation (MAD)2238.5
Skewness-0.0087537714
Sum2.6069267 × 108
Variance6700836.9
MonotonicityNot monotonic
2025-07-06T16:53:32.334814image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5062 16
 
< 0.1%
2609 15
 
< 0.1%
9615 15
 
< 0.1%
9126 15
 
< 0.1%
9930 15
 
< 0.1%
7952 14
 
< 0.1%
6197 14
 
< 0.1%
4571 14
 
< 0.1%
3207 14
 
< 0.1%
3872 14
 
< 0.1%
Other values (8953) 47150
33.4%
(Missing) 93954
66.5%
ValueCountFrequency (%)
1000 3
< 0.1%
1001 4
< 0.1%
1002 3
< 0.1%
1003 4
< 0.1%
1004 3
< 0.1%
1005 5
< 0.1%
1006 7
< 0.1%
1007 5
< 0.1%
1008 3
< 0.1%
1009 6
< 0.1%
ValueCountFrequency (%)
9999 7
< 0.1%
9998 8
< 0.1%
9997 7
< 0.1%
9996 5
< 0.1%
9995 5
< 0.1%
9994 6
< 0.1%
9993 5
< 0.1%
9992 7
< 0.1%
9991 8
< 0.1%
9990 7
< 0.1%

Card Type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size7.5 MiB
Credit
47296 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters283776
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit
2nd rowCredit
3rd rowCredit
4th rowCredit
5th rowCredit

Common Values

ValueCountFrequency (%)
Credit 47296
33.5%
(Missing) 93954
66.5%

Length

2025-07-06T16:53:32.370702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.388146image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
credit 47296
100.0%

Most occurring characters

ValueCountFrequency (%)
C 47296
16.7%
r 47296
16.7%
e 47296
16.7%
d 47296
16.7%
i 47296
16.7%
t 47296
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 47296
16.7%
r 47296
16.7%
e 47296
16.7%
d 47296
16.7%
i 47296
16.7%
t 47296
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 47296
16.7%
r 47296
16.7%
e 47296
16.7%
d 47296
16.7%
i 47296
16.7%
t 47296
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 47296
16.7%
r 47296
16.7%
e 47296
16.7%
d 47296
16.7%
i 47296
16.7%
t 47296
16.7%

Token ID
Text

Missing 

Distinct89597
Distinct (%)94.9%
Missing46844
Missing (%)33.2%
Memory size6.7 MiB
2025-07-06T16:53:32.513429image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters944060
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84954 ?
Unique (%)90.0%

Sample

1st rowTOK_850912
2nd rowTOK_268155
3rd rowTOK_866077
4th rowTOK_244127
5th rowTOK_513827
ValueCountFrequency (%)
tok_962548 4
 
< 0.1%
tok_646979 4
 
< 0.1%
tok_257937 4
 
< 0.1%
tok_974943 4
 
< 0.1%
tok_820511 4
 
< 0.1%
tok_973529 4
 
< 0.1%
tok_769620 4
 
< 0.1%
tok_426828 3
 
< 0.1%
tok_848212 3
 
< 0.1%
tok_265257 3
 
< 0.1%
Other values (89587) 94369
> 99.9%
2025-07-06T16:53:32.672501image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Bank Account Type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing94406
Missing (%)66.8%
Memory size7.6 MiB
Checking
46844 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters374752
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChecking
2nd rowChecking
3rd rowChecking
4th rowChecking
5th rowChecking

Common Values

ValueCountFrequency (%)
Checking 46844
33.2%
(Missing) 94406
66.8%

Length

2025-07-06T16:53:32.703065image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.722486image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
checking 46844
100.0%

Most occurring characters

ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Bank Name
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing94406
Missing (%)66.8%
Memory size7.9 MiB
Bank of Example
46844 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters702660
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank of Example
2nd rowBank of Example
3rd rowBank of Example
4th rowBank of Example
5th rowBank of Example

Common Values

ValueCountFrequency (%)
Bank of Example 46844
33.2%
(Missing) 94406
66.8%

Length

2025-07-06T16:53:32.742731image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:32.761506image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
bank 46844
33.3%
of 46844
33.3%
example 46844
33.3%

Most occurring characters

ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

PayPal Payer ID
Text

Missing 

Distinct36639
Distinct (%)77.8%
Missing94140
Missing (%)66.6%
Memory size5.6 MiB
2025-07-06T16:53:32.881251image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters565320
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27837 ?
Unique (%)59.1%

Sample

1st rowPAYPAL_68156
2nd rowPAYPAL_39472
3rd rowPAYPAL_70561
4th rowPAYPAL_58053
5th rowPAYPAL_14028
ValueCountFrequency (%)
paypal_74333 6
 
< 0.1%
paypal_64956 6
 
< 0.1%
paypal_34329 6
 
< 0.1%
paypal_85624 6
 
< 0.1%
paypal_91684 6
 
< 0.1%
paypal_64341 5
 
< 0.1%
paypal_67055 5
 
< 0.1%
paypal_57454 5
 
< 0.1%
paypal_60070 5
 
< 0.1%
paypal_14087 5
 
< 0.1%
Other values (36629) 47055
99.9%
2025-07-06T16:53:33.042736image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 94220
16.7%
A 94220
16.7%
Y 47110
8.3%
L 47110
8.3%
_ 47110
8.3%
6 24334
 
4.3%
1 24257
 
4.3%
7 24129
 
4.3%
9 24071
 
4.3%
2 24058
 
4.3%
Other values (5) 114701
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 94220
16.7%
A 94220
16.7%
Y 47110
8.3%
L 47110
8.3%
_ 47110
8.3%
6 24334
 
4.3%
1 24257
 
4.3%
7 24129
 
4.3%
9 24071
 
4.3%
2 24058
 
4.3%
Other values (5) 114701
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 94220
16.7%
A 94220
16.7%
Y 47110
8.3%
L 47110
8.3%
_ 47110
8.3%
6 24334
 
4.3%
1 24257
 
4.3%
7 24129
 
4.3%
9 24071
 
4.3%
2 24058
 
4.3%
Other values (5) 114701
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 94220
16.7%
A 94220
16.7%
Y 47110
8.3%
L 47110
8.3%
_ 47110
8.3%
6 24334
 
4.3%
1 24257
 
4.3%
7 24129
 
4.3%
9 24071
 
4.3%
2 24058
 
4.3%
Other values (5) 114701
20.3%

AVS Result Code
Boolean

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size276.0 KiB
True
47296 
(Missing)
93954 
ValueCountFrequency (%)
True 47296
33.5%
(Missing) 93954
66.5%
2025-07-06T16:53:33.065104image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

CVV Result Code
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size7.3 MiB
M
47296 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters47296
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 47296
33.5%
(Missing) 93954
66.5%

Length

2025-07-06T16:53:33.087629image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:33.105720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
m 47296
100.0%

Most occurring characters

ValueCountFrequency (%)
M 47296
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 47296
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 47296
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 47296
100.0%
Distinct130917
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
2025-07-06T16:53:33.256166image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1412500
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121088 ?
Unique (%)85.7%

Sample

1st rowAUTH698092
2nd rowAUTH259525
3rd rowAUTH662545
4th rowAUTH779374
5th rowAUTH375526
ValueCountFrequency (%)
auth479890 4
 
< 0.1%
auth627885 4
 
< 0.1%
auth181464 4
 
< 0.1%
auth685219 4
 
< 0.1%
auth776662 4
 
< 0.1%
auth571739 4
 
< 0.1%
auth344919 4
 
< 0.1%
auth963043 4
 
< 0.1%
auth813614 4
 
< 0.1%
auth682755 4
 
< 0.1%
Other values (130907) 141210
> 99.9%
2025-07-06T16:53:33.452394image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 141250
10.0%
U 141250
10.0%
T 141250
10.0%
H 141250
10.0%
9 86690
 
6.1%
8 86510
 
6.1%
3 86496
 
6.1%
2 86448
 
6.1%
6 86364
 
6.1%
1 86225
 
6.1%
Other values (4) 328767
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1412500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 141250
10.0%
U 141250
10.0%
T 141250
10.0%
H 141250
10.0%
9 86690
 
6.1%
8 86510
 
6.1%
3 86496
 
6.1%
2 86448
 
6.1%
6 86364
 
6.1%
1 86225
 
6.1%
Other values (4) 328767
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1412500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 141250
10.0%
U 141250
10.0%
T 141250
10.0%
H 141250
10.0%
9 86690
 
6.1%
8 86510
 
6.1%
3 86496
 
6.1%
2 86448
 
6.1%
6 86364
 
6.1%
1 86225
 
6.1%
Other values (4) 328767
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1412500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 141250
10.0%
U 141250
10.0%
T 141250
10.0%
H 141250
10.0%
9 86690
 
6.1%
8 86510
 
6.1%
3 86496
 
6.1%
2 86448
 
6.1%
6 86364
 
6.1%
1 86225
 
6.1%
Other values (4) 328767
23.3%
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:33.490508image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:33.535990image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Gateway Fees
Real number (ℝ)

High correlation 

Distinct4995
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.796576
Minimum0.06
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:53:33.580935image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.29
Q16.03
median11.95
Q326.94
95-th percentile42.63
Maximum50
Range49.94
Interquartile range (IQR)20.91

Descriptive statistics

Standard deviation13.173946
Coefficient of variation (CV)0.78432333
Kurtosis-0.60008441
Mean16.796576
Median Absolute Deviation (MAD)8.4
Skewness0.71942814
Sum2372516.3
Variance173.55286
MonotonicityNot monotonic
2025-07-06T16:53:33.623682image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.64 92
 
0.1%
4.84 89
 
0.1%
3.19 85
 
0.1%
3.94 85
 
0.1%
6.85 84
 
0.1%
7.99 84
 
0.1%
7.95 83
 
0.1%
5.61 83
 
0.1%
1.07 83
 
0.1%
5.52 83
 
0.1%
Other values (4985) 140399
99.4%
ValueCountFrequency (%)
0.06 12
 
< 0.1%
0.07 36
< 0.1%
0.08 48
< 0.1%
0.09 30
< 0.1%
0.1 38
< 0.1%
0.11 42
< 0.1%
0.12 46
< 0.1%
0.13 35
< 0.1%
0.14 38
< 0.1%
0.15 42
< 0.1%
ValueCountFrequency (%)
50 2
 
< 0.1%
49.99 6
 
< 0.1%
49.98 6
 
< 0.1%
49.97 4
 
< 0.1%
49.96 14
< 0.1%
49.95 12
< 0.1%
49.94 10
< 0.1%
49.93 15
< 0.1%
49.92 6
 
< 0.1%
49.91 6
 
< 0.1%

Net Settled Amount
Real number (ℝ)

High correlation 

Distinct124790
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.4242
Minimum-5046.2
Maximum4987.37
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:53:33.663966image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5046.2
5-th percentile-2672.5785
Q1822.68
median2206.12
Q33583.4875
95-th percentile4692.4065
Maximum4987.37
Range10033.57
Interquartile range (IQR)2760.8075

Descriptive statistics

Standard deviation2107.6026
Coefficient of variation (CV)1.0739791
Kurtosis1.0941128
Mean1962.4242
Median Absolute Deviation (MAD)1380.015
Skewness-1.0220654
Sum2.7719242 × 108
Variance4441988.9
MonotonicityNot monotonic
2025-07-06T16:53:33.703155image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3385.57 5
 
< 0.1%
3300.21 5
 
< 0.1%
2897.74 5
 
< 0.1%
4796.47 5
 
< 0.1%
2508.85 4
 
< 0.1%
740.26 4
 
< 0.1%
1054.78 4
 
< 0.1%
1727.27 4
 
< 0.1%
2273.48 4
 
< 0.1%
1768.97 4
 
< 0.1%
Other values (124780) 141206
> 99.9%
ValueCountFrequency (%)
-5046.2 1
< 0.1%
-5045.89 1
< 0.1%
-5045.17 1
< 0.1%
-5044.44 1
< 0.1%
-5044.03 1
< 0.1%
-5042.96 1
< 0.1%
-5042.84 1
< 0.1%
-5042.64 1
< 0.1%
-5039.5 1
< 0.1%
-5039.08 1
< 0.1%
ValueCountFrequency (%)
4987.37 1
< 0.1%
4987.35 1
< 0.1%
4987.11 1
< 0.1%
4987.04 1
< 0.1%
4986.94 1
< 0.1%
4986.78 2
< 0.1%
4986.68 1
< 0.1%
4986.35 1
< 0.1%
4986.34 1
< 0.1%
4986.23 1
< 0.1%
Distinct47296
Distinct (%)100.0%
Missing93954
Missing (%)66.5%
Memory size5.6 MiB
2025-07-06T16:53:33.805869image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters567552
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47296 ?
Unique (%)100.0%

Sample

1st rowARN618188908
2nd rowARN591055160
3rd rowARN184492302
4th rowARN314680266
5th rowARN615595718
ValueCountFrequency (%)
arn276300877 1
 
< 0.1%
arn213177541 1
 
< 0.1%
arn669849388 1
 
< 0.1%
arn629677896 1
 
< 0.1%
arn591055160 1
 
< 0.1%
arn184492302 1
 
< 0.1%
arn314680266 1
 
< 0.1%
arn615595718 1
 
< 0.1%
arn594832966 1
 
< 0.1%
arn508354528 1
 
< 0.1%
Other values (47286) 47286
> 99.9%
2025-07-06T16:53:33.935193image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

reference_number
Text

Unique 

Distinct141250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
2025-07-06T16:53:34.105016image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1553750
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141250 ?
Unique (%)100.0%

Sample

1st rowPAY-0000000
2nd rowPAY-0000001
3rd rowPAY-0000002
4th rowPAY-0000003
5th rowPAY-0000004
ValueCountFrequency (%)
pay-0000000 1
 
< 0.1%
pay-0000011 1
 
< 0.1%
pay-0000017 1
 
< 0.1%
pay-0000016 1
 
< 0.1%
pay-0000015 1
 
< 0.1%
pay-0000014 1
 
< 0.1%
pay-0000013 1
 
< 0.1%
pay-0000012 1
 
< 0.1%
pay-0000010 1
 
< 0.1%
pay-0000070 1
 
< 0.1%
Other values (141240) 141240
> 99.9%
2025-07-06T16:53:34.293203image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Interactions

2025-07-06T16:53:30.609969image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.201589image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.338643image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.470931image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.641870image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.234781image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.369874image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.505458image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.675712image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.271922image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.401303image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.540992image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.710800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.305453image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.434519image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:30.575201image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-07-06T16:53:34.321866image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Card BrandCard Last 4 DigitsGateway FeesGateway Response MessageNet Settled AmountPayment Gateway NameTransaction AmountTransaction CurrencyTransaction StatusTransaction Type
Card Brand1.0000.0000.0130.0000.0121.0000.0120.0060.0000.000
Card Last 4 Digits0.0001.0000.0040.0000.0051.0000.0050.0000.0000.000
Gateway Fees0.0130.0041.0000.0000.5940.5080.5970.0040.0010.000
Gateway Response Message0.0000.0000.0001.0000.5770.0000.5770.0001.0001.000
Net Settled Amount0.0120.0050.5940.5771.0000.0001.0000.0040.3000.577
Payment Gateway Name1.0001.0000.5080.0000.0001.0000.0000.0000.0050.000
Transaction Amount0.0120.0050.5970.5771.0000.0001.0000.0040.3010.577
Transaction Currency0.0060.0000.0040.0000.0040.0000.0041.0000.0000.000
Transaction Status0.0000.0000.0011.0000.3000.0050.3010.0001.0001.000
Transaction Type0.0000.0000.0001.0000.5770.0000.5770.0001.0001.000

Missing values

2025-07-06T16:53:30.806860image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-06T16:53:30.988022image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-06T16:53:31.269388image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Gateway Transaction IDPayment Gateway NameTransaction StatusGateway Response CodeGateway Response MessageTransaction AmountTransaction CurrencyTransaction TypeCard BrandCard Last 4 DigitsCard TypeToken IDBank Account TypeBank NamePayPal Payer IDAVS Result CodeCVV Result CodeAuthorization CodeGateway TimestampGateway FeesNet Settled AmountAcquirer Reference Number (ARN)reference_number
0GW-TRX-9972261PayPalsettled0Approved2592.98USDpaymentNaNNaNNaNTOK_850912NaNNaNPAYPAL_68156NaNNaNAUTH6980922025-01-3019.452573.53NaNPAY-0000000
1GW-TRX-8455695GoCardlesssettled0Approved4277.28USDpaymentNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH2595252025-01-1010.694266.59NaNPAY-0000001
2GW-TRX-5184318GoCardlesssettled0Approved2515.78USDpaymentNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH6625452025-02-216.292509.49NaNPAY-0000002
3GW-TRX-8104962Cybersourcesettled0Approved2874.81GBPpaymentVisa9887.0CreditTOK_268155NaNNaNNaNYMAUTH7793742025-01-0428.752846.06ARN618188908PAY-0000003
4GW-TRX-2770667PayPalsettled0Approved350.01USDpaymentNaNNaNNaNTOK_866077NaNNaNPAYPAL_39472NaNNaNAUTH3755262025-03-162.63347.38NaNPAY-0000004
5GW-TRX-8558984Cybersourcesettled0Approved4412.91USDpaymentMastercard9534.0CreditTOK_244127NaNNaNNaNYMAUTH7306632025-02-1044.134368.78ARN591055160PAY-0000005
6GW-TRX-5053867GoCardlesssettled0Approved399.15AUDpaymentNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH5395572025-02-071.00398.15NaNPAY-0000006
7GW-TRX-1689560GoCardlesssettled0Approved1639.12GBPpaymentNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH5805952025-02-204.101635.02NaNPAY-0000007
8GW-TRX-5187601Cybersourcesettled0Approved802.45USDpaymentAmex2535.0CreditTOK_513827NaNNaNNaNYMAUTH1229402025-02-258.02794.43ARN184492302PAY-0000008
9GW-TRX-8963563Cybersourcesettled0Approved4780.71USDpaymentMastercard6108.0CreditTOK_505369NaNNaNNaNYMAUTH5759602025-02-2347.814732.90ARN314680266PAY-0000009
Gateway Transaction IDPayment Gateway NameTransaction StatusGateway Response CodeGateway Response MessageTransaction AmountTransaction CurrencyTransaction TypeCard BrandCard Last 4 DigitsCard TypeToken IDBank Account TypeBank NamePayPal Payer IDAVS Result CodeCVV Result CodeAuthorization CodeGateway TimestampGateway FeesNet Settled AmountAcquirer Reference Number (ARN)reference_number
141240GW-TRX-8740287GoCardlessdisputedC02Chargeback Reversed689.24USDchargeback_reversalNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH4063412025-03-261.72687.52NaNREV-0107910
141241GW-TRX-2880464CybersourcedisputedC02Chargeback Reversed2318.44USDchargeback_reversalMastercard5928.0CreditTOK_629291NaNNaNNaNYMAUTH5453292025-03-0623.182295.26ARN263111260REV-0024103
141242GW-TRX-8746072CybersourcedisputedC02Chargeback Reversed4353.13CADchargeback_reversalAmex8590.0CreditTOK_952318NaNNaNNaNYMAUTH1062252025-03-1143.534309.60ARN135414927REV-0039969
141243GW-TRX-9992588PayPaldisputedC02Chargeback Reversed28.61GBPchargeback_reversalNaNNaNNaNTOK_718010NaNNaNPAYPAL_61698NaNNaNAUTH4010482025-01-310.2128.40NaNREV-0101060
141244GW-TRX-4931445GoCardlessdisputedC02Chargeback Reversed2263.30USDchargeback_reversalNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH5805222025-01-245.662257.64NaNREV-0020178
141245GW-TRX-3674175GoCardlessdisputedC02Chargeback Reversed2573.44USDchargeback_reversalNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH4889422025-03-126.432567.01NaNREV-0025245
141246GW-TRX-7067200CybersourcedisputedC02Chargeback Reversed794.03EURchargeback_reversalAmex8120.0CreditTOK_778198NaNNaNNaNYMAUTH3708222025-01-017.94786.09ARN131111169REV-0093761
141247GW-TRX-1013476PayPaldisputedC02Chargeback Reversed3629.53USDchargeback_reversalNaNNaNNaNTOK_797724NaNNaNPAYPAL_12773NaNNaNAUTH4637382025-02-1327.223602.31NaNREV-0103102
141248GW-TRX-4629535CybersourcedisputedC02Chargeback Reversed2700.28USDchargeback_reversalMastercard8696.0CreditTOK_890568NaNNaNNaNYMAUTH3005572025-02-1027.002673.28ARN894657558REV-0039089
141249GW-TRX-7889594GoCardlessdisputedC02Chargeback Reversed2500.12CADchargeback_reversalNaNNaNNaNNaNCheckingBank of ExampleNaNNaNNaNAUTH9616262025-01-056.252493.87NaNREV-0022422